Hierarchical Federated Graph Attention Networks for Scalable and Resilient UAV Collision Avoidance
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866917080628985856 |
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| author | Shit, Rathin Chandra Subudhi, Sharmila |
| author_facet | Shit, Rathin Chandra Subudhi, Sharmila |
| contents | The real-time performance, adversarial resiliency, and privacy preservation are the most important metrics that need to be balanced to practice collision avoidance in large-scale multi-UAV (Unmanned Aerial Vehicle) systems. Current frameworks tend to prescribe monolithic solutions that are not only prohibitively computationally complex with a scaling cost of $O(n^2)$ but simply do not offer Byzantine fault tolerance. The proposed hierarchical framework presented in this paper tries to eliminate such trade-offs by stratifying a three-layered architecture. We spread the intelligence into three layers: an immediate collision avoiding local layer running on dense graph attention with latency of $<10 ms$, a regional layer using sparse attention with $O(nk)$ computational complexity and asynchronous federated learning with coordinate-wise trimmed mean aggregation, and lastly, a global layer using a lightweight Hashgraph-inspired protocol. We have proposed an adaptive differential privacy mechanism, wherein the noise level $(ε\in [0.1, 1.0])$ is dynamically reduced based on an evaluation of the measured real-time threat that in turn maximized the privacy-utility tradeoff. Through the use of Distributed Hash Table (DHT)-based lightweight audit logging instead of heavyweight blockchain consensus, the median cost of getting a $95^{th}$ percentile decision within 50ms is observed across all tested swarm sizes. This architecture provides a scalable scenario of 500 UAVs with a collision rate of $< 2.0\%$ and the Byzantine fault tolerance of $f < n/3$. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_11616 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hierarchical Federated Graph Attention Networks for Scalable and Resilient UAV Collision Avoidance Shit, Rathin Chandra Subudhi, Sharmila Robotics Artificial Intelligence Machine Learning Multiagent Systems The real-time performance, adversarial resiliency, and privacy preservation are the most important metrics that need to be balanced to practice collision avoidance in large-scale multi-UAV (Unmanned Aerial Vehicle) systems. Current frameworks tend to prescribe monolithic solutions that are not only prohibitively computationally complex with a scaling cost of $O(n^2)$ but simply do not offer Byzantine fault tolerance. The proposed hierarchical framework presented in this paper tries to eliminate such trade-offs by stratifying a three-layered architecture. We spread the intelligence into three layers: an immediate collision avoiding local layer running on dense graph attention with latency of $<10 ms$, a regional layer using sparse attention with $O(nk)$ computational complexity and asynchronous federated learning with coordinate-wise trimmed mean aggregation, and lastly, a global layer using a lightweight Hashgraph-inspired protocol. We have proposed an adaptive differential privacy mechanism, wherein the noise level $(ε\in [0.1, 1.0])$ is dynamically reduced based on an evaluation of the measured real-time threat that in turn maximized the privacy-utility tradeoff. Through the use of Distributed Hash Table (DHT)-based lightweight audit logging instead of heavyweight blockchain consensus, the median cost of getting a $95^{th}$ percentile decision within 50ms is observed across all tested swarm sizes. This architecture provides a scalable scenario of 500 UAVs with a collision rate of $< 2.0\%$ and the Byzantine fault tolerance of $f < n/3$. |
| title | Hierarchical Federated Graph Attention Networks for Scalable and Resilient UAV Collision Avoidance |
| topic | Robotics Artificial Intelligence Machine Learning Multiagent Systems |
| url | https://arxiv.org/abs/2511.11616 |